# Deep Generalized Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/1702.02519 · HTML: https://mlsanity.com/paper/1702.02519/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 30 of 33)

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- [Interpretable Deep Learning Methods for Multiview Learning](https://mlsanity.com/paper/2302.07930/) (2023)
- [SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-omics Integration](https://mlsanity.com/paper/2204.09045/) (2022)
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- [Self Supervised Correlation-based Permutations for Multi-View Clustering](https://mlsanity.com/paper/2402.16383/) (2024)
- [Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization](https://mlsanity.com/paper/2411.00383/) (2024)
- [Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data](https://mlsanity.com/paper/2502.18756/) (2025)
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- [Multi-view Deep One-class Classification: A Systematic Exploration](https://mlsanity.com/paper/2104.13000/) (2021)
- [Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis](https://mlsanity.com/paper/2109.12400/) (2021)
- [Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints](https://mlsanity.com/paper/2206.04949/) (2022)
- [How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?](https://mlsanity.com/paper/2211.13309/) (2022)
- [A deep learning pipeline for cross-sectional and longitudinal multiview data integration](https://mlsanity.com/paper/2312.01238/) (2023)
- [Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning](https://mlsanity.com/paper/2409.13887/) (2024)
- [Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations](https://mlsanity.com/paper/2504.11610/) (2025)
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## Abstract (arXiv)

We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.